Synced from Ashby · Apr 21

Machine Learning Researcher, Multimodal LLMs

Bland AISan FranciscoPosted Apr 21, 2026
Research ScientistMid
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Mirrored from Bland AI's own Ashby careers system · refreshed hourly

$160k–$280k
Compensation
14
Other open Bland AI roles
Apr 21
Posted
Ashby
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Job description

Machine Learning Researcher, Multimodal LLMs

Location: San Francisco, CA or Remote

About Bland

At Bland.com, our mission is to empower enterprises to build AI phone agents at scale. Voice is quickly becoming the primary interface between businesses and their customers, and we are building the models and infrastructure that make those interactions feel natural, reliable, and genuinely human.

We’ve raised $100M from leading investors including Emergence Capital, Scale Venture Partners, Y Combinator, and founders of Twilio, Affirm, and ElevenLabs.

The Role

We are looking for someone to contribute to the development of our next-generation multimodal LLM stack, combining speech, text, tools, and real-time reasoning into a single unified system. You’ll be responsible for building industry-leading conversational AI models that power Bland's agent, and taking them all the way from idea to production.

At Bland, we're not just thinking about text modeling. You will define how our agents listen, think, and act in real time, integrating streaming audio, tool execution, and dynamic context into a single coherent system. You will take ideas from research through production systems serving millions of calls per day.

What Makes You a Great Fit

Strong LLM / Multimodal Background

  • Experience with LLMs, multimodal models, or speech-language systems

  • Deep understanding of prompting, fine-tuning, and alignment techniques

  • Familiarity with neural audio codecs and modern multimodal LLM techniques

Fast Experimental Loop

  • You can go from idea → dataset → experiment → conclusion in days

  • You know how to design experiments that actually answer the question

Product Intuition

  • Strong sense for what makes an interaction feel natural vs robotic

  • Ability to translate abstract modeling ideas into user-facing improvements

Builder Mentality

  • You take ownership from research through deployment

  • You thrive in ambiguous, fast-moving environments

  • You care about impact, not just elegance

How You Show Up

  • You think in systems, not just models

  • You obsess over latency, correctness, and real-world behavior

  • You are comfortable discarding ideas quickly when data disagrees

  • You push toward simple abstractions for complex problems

Bonus Points

  • Experience with real-time voice systems or conversational AI

  • Background in tool-using agents or agent frameworks

  • Experience with multimodal datasets (audio + text + actions)

  • Contributions to LLM or speech-related research or open source

Compensation & Benefits

  • Competitive salary: $160,000 – $280,000

  • Meaningful equity

  • Full healthcare, dental, vision

  • Office in Levi's Plaza, SF

  • High autonomy, high impact

Additional Information
Bland is an equal opportunity employer. We are committed to proving equal employment opportunities to all qualified applicants and employees and do not discriminate based on any legally protected characteristic.

Bland participates in the E-Verify employment verification program. All new hires are required to complete the Form I-9, and employment will be verified though E-Verify as part of the onboarding process.

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Based on publicly available information, candidates applying through Ashby for roles at Bland AI can typically expect a structured online application process beginning with a form capturing resume details, work history, and sometimes short responses to role-specific questions. Ashby-based processes commonly include an initial recruiter or hiring manager screen, followed by additional stages that may include technical, case-based, or role-specific assessments depending on the position—whether in engineering, sales, or customer-facing functions. Candidates may also encounter take-home exercises or live interviews with team members before a final decision stage. Response times vary by company and role, and communication may occur via email or directly through the Ashby candidate portal. As with most ATS platforms, applicants should ensure application materials are accurate and tailored, since automated filtering may be used in early screening stages.

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Machine Learning Researcher, Multimodal LLMs
Bland AI · San Francisco
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